The main goals for this week were:

  1. Examine patterns of diversity within the cyanobacteria
    • How many OTUs of each genus are there?
    • Shifts in OTUs over time - is MC different than other cyanos?
    • How do shifts in oligotypes compare to OTUs?
  2. look at non-abundance weighted measures of dissimilarity to see if there are real turnover events in the community throughout the season

  3. Explore Erens MED analysis and rerun some ordinations to see if they are similar overall

Stacked barplot

Time series of phylum community composition across filter fractions and sampling stations

Ordinations

Unconstrained

Bray-Curtis

theme_set(theme_bw())

myord(
    physeq = moth_good, 
    fraction = "CNA", 
    method = "PCoA", 
    distance = "bray", 
    title = "Full community Bray-Curtis PCoA"
)

myord(
    physeq = moth_good, 
    fraction = "100LTR", 
    method = "PCoA", 
    distance = "bray", 
    title = "100um Bray-Curtis PCoA"
)

myord(
    physeq = moth_good, 
    fraction = "53LTR", 
    method = "PCoA", 
    distance = "bray", 
    title = "53um Bray-Curtis PCoA"
)

myord(
    physeq = moth_good, 
    fraction = "3NA", 
    method = "PCoA", 
    distance = "bray", 
    title = "3um Bray-Curtis PCoA"
)

myord(
    physeq = moth_good, 
    fraction = "22NA", 
    method = "PCoA", 
    distance = "bray", 
    title = ".22um Bray-Curtis PCoA"
)

Sorenson

myord(
    physeq = moth_good, 
    fraction = "CNA", 
    method = "PCoA", 
    distance = "sor", 
    title = "Full community Sorenson PCoA"
)

myord(
    physeq = moth_good, 
    fraction = "100LTR", 
    method = "PCoA", 
    distance = "sor", 
    title = "100um Sorenson PCoA"
)

myord(
    physeq = moth_good, 
    fraction = "53LTR", 
    method = "PCoA", 
    distance = "sor", 
    title = "53um Sorenson PCoA"
)

myord(
    physeq = moth_good, 
    fraction = "3NA", 
    method = "PCoA", 
    distance = "sor", 
    title = "3um Sorenson PCoA"
)

myord(
    physeq = moth_good, 
    fraction = "22NA", 
    method = "PCoA", 
    distance = "sor", 
    title = ".22um Sorenson PCoA"
)

Constrained

Do a permutational ANOVA on constrained axes used in ordination

anova(cap.ord)
## Permutation test for capscale under reduced model
## Permutation: free
## Number of permutations: 999
## 
## Model: capscale(formula = distance ~ ParMC + Nitrate + SRP + LogPhyco + Ammonia + DOC + pH + H2O2, data = data)
##          Df Variance      F Pr(>F)    
## Model     8   3.2952 5.0977  0.001 ***
## Residual 42   3.3936                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Cyanobacteria analysis

Genus diversity

How many reads of different genera are there?

print(cyano_sums)
##       Microcystis     Synechococcus     Pseudanabaena      unclassified 
##            361375            214905             72053             41016 
##          Anabaena      unclassified     Aphanizomenon      unclassified 
##             22876             15002               327              1030 
##    Cyanobacterium      unclassified      unclassified          Snowella 
##               324               328               369               218 
##      unclassified      Planktothrix      unclassified            Nostoc 
##               908               345               114                18 
##      Oscillatoria        Phormidium      Merismopedia         Cyanobium 
##                37               122                31                 6 
##      Leptolyngbya      unclassified      Chamaesiphon         Calothrix 
##                25                98                 9                 9 
##      unclassified Planktothricoides      unclassified      unclassified 
##                 1                 2                68                11 
## Chroococcidiopsis       Gloeocalita 
##                96                 4

How many OTUs are there of each genus?

gen <- tax_table(cyanos)[,6]
table(gen)
## gen
##          Anabaena     Aphanizomenon         Calothrix      Chamaesiphon 
##                 9                 1                 2                 3 
## Chroococcidiopsis    Cyanobacterium         Cyanobium       Gloeocalita 
##                 4                 1                 3                 1 
##      Leptolyngbya      Merismopedia       Microcystis            Nostoc 
##                 6                 1                10                 1 
##      Oscillatoria        Phormidium Planktothricoides      Planktothrix 
##                 1                 3                 1                 2 
##     Pseudanabaena          Snowella     Synechococcus      unclassified 
##                 5                 2                10               257

OTU vs Oligotype barplots

Conclusions

  1. Examine patterns of diversity within the cyanobacteria
    • How many OTUs of each genus are there?
    • Shifts in OTUs over time - is MC different than other cyanos?
    • How do shifts in oligotypes compare?

OTU analysis: There are up to 10 OTUs for different genera, but there is usually only a couple abundant OTUs (others are spurious??). Anabaena has 2, Synechococcus has 4, Microcystis and pseudanabaena have only 1 dominant OTU.

There are no obvious patterns of shifts in OTU populations over time, except for synechococcus. The orange OTU only appears in the first part of the season and the red appears in the second. Also, it seems like synechococcus blooms slightly ahead of microcystis.

Both OTU 49 and 367 had no matches in NCBI to known things

Oligotype analysis: Synechococcus gets the award for most interesting organism this week!!! Already at the OTU level we can see more diversity in syn than any other cyano genera, but after oligotyping there is an astonishing amount of diversity. Is this real?? The BLAST results from each of the oligotypes show mixed results; Some were listed as synechococcus others as unclassified cyanobacteria. I went back to my taxonomy file and got rid of all sequences with a bootstrap value below 90 for synechococcus. The entropy analysis still reveals almost the same amount of diversity

  1. look at non-abundance weighted measures of dissimilarity to see if there are real turnover events in the community throughout the season

It looks like most changes in community composition are due to changes in relative abundance rather than species turnover

  1. Explore Erens MED analysis and rerun some ordinations to see if they are similar overall

A few problems with MED

  1. MED paper reccomends running it with -M parameter N/10,000 or larger (i.e about 1500 for my dataset) Whats the point of this pipeline if youre going to throw away all of your rare (not even that rare) OTUs?

  2. Entropy is biased based on library size. Since these samples have very different sequencing depths, I would need to normalize before running it to get accurate results

Ultimately MED is just an algorithmic improvement to Oligotyping, not a way to avoid OTU clustering with whole community data